Data-Driven Extraction and Composition of Secondary Dynamics in Facial Performance Capture

2. The computer-implemented method of claim 1, wherein generating the first set of values comprises identifying a plurality of non-rigid points included in the first geometric model that correspond to the one or more non-rigid portions of the face of the performer.

3. The computer-implemented method of claim 2, wherein generating the first set of values further comprises comparing the plurality of non-rigid points with a plurality of points included in a default geometric model associated with the performer.

4. The computer-implemented method of claim 1, wherein generating the second set of values comprises identifying a plurality of rigid points included in the first geometric model that correspond to the one or more rigid portions of the face of the performer.

5. The computer-implemented method of claim 4, wherein generating the second set of values further comprises determining, for each rigid point included in the plurality of rigid points, one or more positions of the rigid point over an interval of time.

6. The computer-implemented method of claim 1, wherein the first expression model is trained to receive a set of velocity values associated with the one or more rigid portions and generate a set of delta values associated with the one or more non-rigid portions.

7. The computer-implemented method of claim 1, further comprising generating a first prediction model based on the first expression model, wherein the first prediction model is configured to quantify different secondary dynamics for different facial expressions.

8. The computer-implemented method of claim 7, wherein generating the first prediction model comprises generating a set of blendshape weights based on at least the first geometric model.

10. The computer-implemented method of claim 9, further comprising generating a first prediction model based on the first expression model and the second expression model, wherein the first prediction model is configured to quantify different secondary dynamics for different facial expressions.

12. The one or more non-transitory computer-readable medium of claim 11, wherein generating the first set of values comprises identifying a plurality of non-rigid points included in the first geometric model that correspond to the one or more non-rigid portions of the face of the performer.

13. The one or more non-transitory computer-readable medium of claim 12, wherein generating the first set of values further comprises comparing the plurality of non-rigid points with a plurality of points included in a default geometric model associated with the performer.

14. The one or more non-transitory computer-readable medium of claim 11, wherein generating the second set of values comprises identifying a plurality of rigid points included in the first geometric model that correspond to the one or more rigid portions of the face of the performer.

15. The one or more non-transitory computer-readable medium of claim 14, wherein generating the second set of values further comprises determining, for each rigid point included in the plurality of rigid points, one or more positions of the rigid point over an interval of time.

16. The one or more non-transitory computer-readable medium of claim 11, wherein the first expression model predicts an amount of secondary dynamics associated with the first facial expression in a first dimension, and the instructions, when executed by the one or more processors, further cause the one or more processors to perform the step of training a second expression model based on the first set of values and the second set of values, wherein the second expression model is usable to predict an amount of secondary dynamics associated with the first facial expression in a second dimension.

18. The one or more non-transitory computer-readable medium of claim 17, wherein combining the first expression model and the second expression model comprises generating a first set of weights associated with the first expression model and a second set of weights associated with the second expression model.

19. The one or more non-transitory computer-readable medium of claim 17, wherein combining the first expression model and the second expression model comprises generating one or more sets of weights based on the training data, wherein each set of weights is associated with a different facial region of the face of the performer.